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104 lines
3.5 KiB
104 lines
3.5 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/math/sequence_pooling.h"
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#include "paddle/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <typename T>
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class MaxSeqPoolFunctor<platform::CPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::LoDTensor& input, framework::Tensor* output,
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framework::Tensor* index) {
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auto in_dims = input.dims();
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auto out_dims = output->dims();
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auto idx_dims = index->dims();
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PADDLE_ENFORCE_GT(in_dims.size(), 1);
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PADDLE_ENFORCE_GT(out_dims.size(), 1);
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for (int64_t i = 1; i < in_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, out_dims);
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auto starts = input.lod()[0];
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const T* in_data = input.data<T>();
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T* out_data = output->data<T>();
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int* max_index = index->data<int>();
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int64_t num_seq = out_dims[0];
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int64_t dim = output->numel() / num_seq;
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for (int64_t i = 0; i < num_seq; ++i) {
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for (int64_t k = 0; k < dim; ++k) {
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out_data[i * dim + k] = in_data[starts[i] * dim + k];
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max_index[i * dim + k] = starts[i];
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}
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for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) {
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for (int64_t k = 0; k < dim; ++k) {
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if (in_data[j * dim + k] > out_data[i * dim + k]) {
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out_data[i * dim + k] = in_data[j * dim + k];
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max_index[i * dim + k] = j;
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}
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}
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}
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}
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}
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};
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template <typename T>
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class MaxSeqPoolGradFunctor<platform::CPUPlace, T> {
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public:
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void operator()(const platform::DeviceContext& context,
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const framework::Tensor& out_grad,
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const framework::Tensor& index,
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framework::LoDTensor* in_grad) {
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auto og_dims = out_grad.dims();
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auto ig_dims = in_grad->dims();
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auto idx_dims = index.dims();
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PADDLE_ENFORCE_GT(og_dims.size(), 1);
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PADDLE_ENFORCE_GT(ig_dims.size(), 1);
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for (int64_t i = 1; i < og_dims.size(); ++i) {
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PADDLE_ENFORCE_EQ(og_dims[i], ig_dims[i]);
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}
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PADDLE_ENFORCE_EQ(idx_dims, og_dims);
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const T* og_data = out_grad.data<T>();
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const int* max_index = index.data<int>();
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T* ig_data = in_grad->data<T>();
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SetConstant<platform::CPUPlace, T> set_zero;
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set_zero(context, in_grad, static_cast<T>(0.0));
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int64_t num_seq = og_dims[0];
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int64_t dim = out_grad.numel() / num_seq;
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for (int64_t i = 0; i < num_seq; ++i) {
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for (int64_t j = 0; j < dim; ++j) {
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int step_id = max_index[i * dim + j];
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ig_data[step_id * dim + j] = og_data[i * dim + j];
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}
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}
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}
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};
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template class MaxSeqPoolFunctor<platform::CPUPlace, float>;
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template class MaxSeqPoolFunctor<platform::CPUPlace, double>;
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template class MaxSeqPoolGradFunctor<platform::CPUPlace, float>;
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template class MaxSeqPoolGradFunctor<platform::CPUPlace, double>;
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} // namespace math
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} // namespace operators
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} // namespace paddle
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